Abstract
Background
There is still no study on the use of the SARC-CalF questionnaire for older critically ill patients. Moreover, there is limited evidence on whether a combination of sarcopenia and frailty can provide incremental improvements in risk stratification for older critically ill patients.
Methods
A total of 653 patients older than 60 years were recruited. We used the clinical frailty scale (CFS) and SARC-CalF questionnaire to assess the frailty status and sarcopenia risk, respectively, of older patients shortly after admission to the ICU. The effect of frailty and sarcopenia risk on ICU mortality and 30-day mortality was evaluated.
Results
A total of 147 (22.5%) patients died in the ICU, and 187 (28.6%) patients died within 30 days after ICU admission. The CFS score was associated with increased ICU mortality [per 1-score increase: odds ratio (OR) = 1.222, 95% confidential interval (CI): 1.003–1.489] and 30-day mortality (per 1-score increase: OR = 1.307, 95% CI: 1.079–1.583). The SARC-CalF score was also associated with increased ICU mortality (per 1-score increase: OR = 1.204, 95% CI: 1.120–1.294) and 30-day mortality (per 1-score increase: OR = 1.247, 95% CI: 1.163–1.337). The addition of the CFS + SARC-CalF score to Acute Physiology and Chronic Health Evaluation (APACHE) II improved discrimination and reclassified ICU and 30-day mortality risk.
Conclusions
Sarcopenia risk assessed by the SARC-CalF questionnaire provided independent prognostic information for older critically ill patients. A combination of sarcopenia and frailty improved the prediction of mortality for older critically ill patients and thus might be useful in the clinical decision-making process.
Keywords: Frailty, Sarcopenia, Critically ill, Older patients, Mortality
1. Background
Risk stratification for critically ill patients is of great importance for providing appropriate interventions and improving prognosis. Several disease severity scoring systems, such as Acute Physiology and Chronic Health Evaluation (APACHE) [1] and Oxford Acute Severity of Illness Score (OASIS) [2], have been developed for stratifying patients admitted to the intensive care unit (ICU). In these risk scoring systems, patient age is always an important variable. However, the functional status and geriatric syndromes of older adults, which have emerged in recent studies as important prognosis factors for a wide range of diseases, are not usually considered.
With the increasing aging population, the age of patients admitted to the ICU is also increasing concomitantly. The number of patients with geriatric conditions such as sarcopenia and frailty is not negligible. Geriatric syndromes can capture prognosis information beyond age and traditional risk factors. The association between sarcopenia, which is characterized by the age-related loss of muscle mass and function performance [3], and adverse prognostic outcomes (e.g., increased ICU mortality, and prolonged duration of ICU or hospital stay) has been well-established among critically ill patients [[4], [5], [6], [7]]. Frailty, defined as an age-related syndrome with declined physiologic reserve [8], has also been consistently reported to be associated with higher ICU, hospital, and long-term mortality among critically ill patients [[9], [10], [11]].
However, previous studies focusing on sarcopenia in ICU settings mainly utilized opportunistic CT scans to measure muscle mass and/or muscle quality, and are all performed post-hoc [12]. As a result, the sarcopenia status of patients cannot be determined immediately after their admission to the ICU to provide timely prognostic information. In addition, there is limited evidence of the incremental value of a combination of frailty and sarcopenia to disease severity scoring systems for older critically ill patients.
In this study, we used the SARC-CalF questionnaire (a widely used screening tool for sarcopenia) and clinical frailty scale (CFS) to assess the sarcopenia status and frailty status, respectively, of older patients shortly after their admission to the ICU. We aimed to determine whether the SARC-CalF score and CFS score are associated with mortality risk and can provide incremental improvements in risk stratification for older critically ill patients.
2. Materials & methods
2.1. Study population and setting
From May 2023 to December 2023, we prospectively and consecutively recruited 653 patients aged ≥ 60 years who were referred to the emergency ICU (EICU) at the Henan Provincial People’s Hospital. Patients who did not stay in the EICU for at least 24 h or with terminal tumors and cachexia were excluded. This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and approved by the Institutional Review Board of Henan Provincial People’s Hospital (2021-No-00049). Written informed consent was obtained from all included patients and/or their proxies before study participation.
2.2. Data collection
We retrieved all of the included patients’ data from their electronic medical records, including ICU admission diagnosis, admission APACHE II score (detailed scoring rules were shown in Table S1), comorbidities (chronic cardiovascular disorders, chronic respiratory disorders, chronic renal failure, diabetes, and cancer), use of organ support [vasopressors, mechanical ventilation (MV) treatment, and continuous renal replacement treatment (CRRT)], transfusion (red blood cells, platelet, and fresh frozen plasma) during ICU stay, ICU length-of-stay (LOS), and mortality. The survival status at 30 days after ICU admission was acquired from the medical records if the patients’ LOS was ≥ 30 days or from a telephone follow-up if the patients’ LOS was <30 days and survived to discharge.
2.3. Defining and assessing frailty
The CFS [13], which is a multidimensional frailty assessment tool that can be easily implemented in different clinical setting including ICU, was applied to assess the patients’ frailty status at ICU admission. The CFS ranges from 1 (very fit) to 9 (terminally ill), with increasing scores indicating higher levels of frailty. Information used to assess frailty status was obtained through clinical examination, review of the medical records, and interview with the patients or their proxy. Scores were assigned by trained nurses according to their clinical judgment based on the patients’ overall health status 3 months prior to hospital admission. In the present study, frailty was defined as a CFS score ≥ 5 points. Detailed method of CFS assessment was shown in Fig. S1).
2.4. Defining and assessing sarcopenia
The SARC-CalF questionnaire [14], which adds calf circumference (CC) to the original five components (strength, assistance with walking, rising from a chair, climbing stairs, and falls) of SARC-F, was used to assess the risk of sarcopenia of the enrolled patients.
Previous study showed that the SARC-CalF had good screening performance for sarcopenia [14]. Each of the original five items was scored ranging from 0 to 2 points; the CC item was scored 10 points if CC ≤ 33 cm for females and CC ≤ 34 cm for males and scored 0 point if CC was higher than cut-off points. The SARC-CalF in total ranged from 0 to 20, with a score of 11–20 indicating a high risk of sarcopenia. CC was measured within 24 h of ICU admission. Detailed method of SARC-CalF assessment was shown in Table S2.
2.5. Statistical analysis
Baseline characteristics are presented as medians and quartiles (quartile 1; quartile 3) for continuous variables due to non-normal distribution or frequency with percentages for categorical variables. Differences in the baseline characteristics and outcomes between groups (frail group and sarcopenic risk group) were analyzed using Mann-Whitney U test for continuous variables and χ2 test for categorical variables. Multivariable logistic regression models were used to investigate the association of CFS score/frailty and SARC-CalF score/sarcopenia risk with mortality outcomes. We also investigated the mortality risk of patients with both frailty and sarcopenia risk by categorizing the patients into 3 groups: both frailty and sarcopenia risk, only frailty or only sarcopenia risk, and no frailty and sarcopenia risk. Age, sex, and APACHE II score were included in the multivariable adjusted models. We calculated the Harrell’s C statistic to assess the predictive performance of APACHE II, CFS, SARC-CalF, APACHE II + CFS, APACHE II + SARC-CalF, and APACHE II + CFS + SARC-CalF for mortality outcomes. We calculated the ΔC-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) to determine the incremental predictive value of adding the CFS, SARC-CalF, or CFS + SARC-CalF (as a continuous variable) to APACHE II for ICU and 30-day mortality. Statistical analyses were conducted using SPSS 26.0 for Windows (SPSS Inc., Chicago, IL, USA) and R (version 4.1.2). P < 0.05 was considered statistically significant.
3. Results
3.1. Characteristics of study participants
As shown in Table 1, a total of 653 older critically ill patients were included in this study, comprising 399 males (61.1%). The median age of the study population was 69 years (IQR 65–77). The ICU mortality rate was 22.5% (n = 147), and the 30-day mortality rate was 28.6% (n = 187). Frailty was diagnosed in 172 patients, and 356 patients had sarcopenia risk, and 145 patients had both frailty and sarcopenia risk (Figure S2). When patients were grouped according to frailty status, we found that frail patients tended to be older, had a higher prevalence of chronic cardiovascular disorders, chronic respiratory disorders, and chronic renal failure, and had higher APACHE II and SARC-CalF scores. In addition, a higher proportion of them received MV during ICU stay. Furthermore, they had higher ICU mortality and 30-day mortality rate and longer ICU LOS. When patients were grouped according to sarcopenia risk status, we found that patients with high sarcopenia risk tended to be older, were likely female, had a higher prevalence of chronic cardiovascular disorders, chronic renal failure, and cancer, and had higher APACHE II and CFS scores. In addition, a higher proportion of them received MV and vasopressors during ICU stay. Furthermore, they had higher ICU mortality and 30-day mortality rate and longer ICU LOS.
Table 1.
Demographic information and clinical features of the included critically ill patients.
| Variable | Total (N = 653) | Frail (N = 172) | Non-frail (N = 481) | P-value | Sarcopenic (N = 356) | Non-sarcopenic (N = 297) | P-value |
|---|---|---|---|---|---|---|---|
| Age, years, median (IQR) | 69 (65−77) | 78 (68−84) | 68 (64−74) | <0.001 | 73 (68−81) | 67 (64−71) | <0.001 |
| Male (n, %) | 399 (61.1) | 105 (61.0) | 294 (61.1) | 0.624 | 193 (54.2) | 206 (69.4) | <0.001 |
| Admission type, (n, %) | 0.014 | 0.022 | |||||
| Medical admission | 544 (83.3) | 146 (84.9) | 394 (81.9) | 284 (79.8) | 260 (87.5) | ||
| Emergency surgery | 50 (7.7) | 17 (9.9) | 29 (6.0) | 35 (9.8) | 15 (5.1) | ||
| Elective surgery | 59 (9.0) | 9 (5.2) | 58 (12.1) | 37 (10.4) | 22 (7.4) | ||
| Comorbidities, (n, %) | |||||||
| Chronic cardiovascular disorders | 183 (28.0) | 60 (38.9) | 123 (25.6) | 0.020 | 117 (32.9) | 66 (22.2) | 0.003 |
| Chronic respiratory disorders | 98 (15.0) | 34 (19.8) | 64 (13.3) | 0.042 | 60 (16.9) | 38 (12.8) | 0.148 |
| Chronic renal failure | 79 (12.1) | 31 (18.0) | 48 (10.0) | 0.005 | 59 (16.6) | 20 (6.7) | <0.001 |
| Diabetes | 107 (16.4) | 29 (16.8) | 78 (16.2) | 0.845 | 54 (15.2) | 53 (17.8) | 0.358 |
| Cancer | 33 (5.1) | 11 (6.4) | 22 (4.6) | 0.349 | 25 (7.0) | 8 (2.7) | 0.012 |
| APACHE II scores, median (IQR) | 31 (24−39) | 34 (24−41) | 29 (24−37) | 0.004 | 33 (25−40) | 29 (23−36) | <0.001 |
| CFS scores, median (IQR) | 4 (3−5) | 6 (5−7) | 4 (3−4) | <0.001 | 4 (4−6) | 4 (3−4) | <0.001 |
| Frailty, (n, %) | 172 (26.3) | – | – | 145 (40.7) | 27 (9.1) | <0.001 | |
| SARC-Calf score, median (IQR) | 12 (4−14) | 16 (14−18) | 10 (2−12) | <0.001 | 14 (12−16) | 3 (1−9) | <0.001 |
| Sarcopenia, (n, %) | 356 (54.5) | 145 (84.3) | 211 (43.3) | <0.001 | – | – | |
| CalF cm, median (IQR) | 31 (28−34) | 28.0 (26.0−31.0) | 32.0 (29.5−34.0) | <0.001 | 29 (27−31) | 34 (32−36) | <0.001 |
| Organ support during ICU stay | |||||||
| MV (n, %) | 220 (33.7) | 69 (40.1) | 151 (31.4) | 0.038 | 161 (45.2) | 59 (19.9) | <0.001 |
| CRRT (n, %) | 87 (13.3) | 28 (16.3) | 59 (12.2) | 0.184 | 54 (15.2) | 33 (11.1) | 0.129 |
| Vasopressors (n, %) | 150 (23.0) | 45 (26.2) | 105 (21.8) | 0.246 | 71 (19.9) | 79 (26.6) | 0.044 |
| Any blood transfusion (n, %) | 78 (11.9) | 22 (12.8) | 56 (11.6) | 0.690 | 37 (10.4) | 41 (13.8) | 0.181 |
| Outcomes | |||||||
| ICU mortality | 147 (22.5) | 76 (43.9) | 71 (14.8) | <0.001 | 123 (34.6) | 24 (8.1) | <0.001 |
| 30-days mortality | 187 (28.6) | 97 (56.4) | 90 (18.7) | <0.001 | 158 (44.4) | 29 (9.8) | <0.001 |
| ICU LOS, days, median (IQR) | 4.4 (3.5−6.5) | 4.7 (3.6−6.6) | 4.3 (3.5−6.3) | 0.021 | 4.4 (3.5−6.6) | 4.1 (3.5−6.0) | 0.040 |
IQR interquartile 25–75% range, APACHE acute physiology and chronic health evaluation, CFS clinical frailty scale, ICU intensive care unit, MV mechanical ventilation, CRRT continuous renal replacement therapy, LOS length-of-stay.
3.2. Association of frailty and sarcopenia risk with mortality outcomes
A total of 147 patients died during ICU stay (22.5%). The mortality rate was higher among frail patients than among non-frail patients (43.9% vs. 14.8%). The multivariate model showed that age, gender, and APACHE II score were independent risk factors for ICU mortality (Table 2). The CFS score was associated with increased ICU mortality as both a continuous variable [per 1-score increase: odds ratio (OR) = 1.222, 95% confidential interval (CI): 1.003–1.489] and a categorical variable (frailty risk vs. no frailty risk: OR = 2.598, 95% CI: 1.616–4.176) in the multivariable model. The SARC-CalF score was also associated with increased ICU mortality as both a continuous variable (per 1-score increase: OR = 1.204, 95% CI: 1.120–1.294) and a categorical variable (sarcopenia risk vs. no sarcopenia risk: OR = 3.433, 95% CI: 1.953–6.035) in the multivariable model.
Table 2.
Association of frailty and sarcopenia with mortality among critically ill patients aged ≥ 60 years.
| ICU mortality |
30-days mortality |
|||
|---|---|---|---|---|
| Univariate model | Adjusted modela | Univariate model | Adjusted modela | |
| Age | 1.082 (1.062−1.103) | 1.042 (1.019−1.066) | 1.084 (1.065−1.104) | 1.041 (1.019−1.064) |
| Sex | 1.126 (0.770−1.646) | 1.694 (1.050−2.734) | 1.056 (0.745−1.498) | 1.704 (1.081−2.687) |
| APACHE II score | 1.114 (1.090−1.139) | 1.111 (1.084−1.138) | 1.097 (1.076−1.119) | 1.095 (1.071−1.120) |
| CFS score | 1.833 (1.598−2.102) | 1.222 (1.003−1.489) | 2.041 (1.774−2.347) | 1.307 (1.079−1.583) |
| Frailty | 4.572 (3.088−6.769) | 2.598 (1.616−4.176)b | 5.619 (3.848−8.205) | 3.169 (2.018−4.978)b |
| SARC-CalF score | 1.274 (1.206−1.346) | 1.204 (1.120−1.294) | 1.333 (1.259−1.410) | 1.247 (1.163−1.337) |
| Sarcopenia | 6.005 (3.749−9.617) | 3.433 (1.953−6.035)b | 7.374 (4.766−11.410) | 4.367 (2.611−7.304)b |
APACHE acute physiology and chronic health evaluation, CFS clinical frailty scale.
Adjusted model included age, sex, APACHE II score, CFS score and SARC-CalF score.
Adjusted model included age, sex, APACHE II score, frailty and sarcopenia.
A total of 187 patients died within 30 days after ICU admission (28.6%). The 30-day mortality rate was higher among patients with sarcopenia risk than among those without sarcopenia risk (34.6% vs. 8.1%). Age, sex, and APACHE II score were significantly associated with an increased risk of 30-day mortality (Table 2). In the multivariable model, the CFS score was associated with increased 30-day mortality as both a continuous variable (per 1-score increase: OR = 1.307, 95% CI: 1.079–1.583) and a categorical variable (frailty risk vs. no frailty risk: OR = 3.169, 95% CI: 2.018–4.978). We also found that the SARC-CalF score was associated with increased 30-day mortality as both a continuous variable (per 1-score increase: OR = 1.247, 95% CI: 1.163–1.337) and a categorical variable (sarcopenia risk vs. no sarcopenia risk: OR = 4.367, 95% CI: 2.611–7.304).
In the multivariable model, patients who had both frailty and sarcopenia risk had 8-fold increased risk of ICU mortality and 12-fold increased risk of 30-day mortality compared to those without frailty and sarcopenia risk (Table 3).
Table 3.
Association of frailty and sarcopenia with mortality among critically ill patients aged ≥ 60 years (Patients in 3 groups).
| ICU mortality |
30-days mortality |
|||
|---|---|---|---|---|
| Univariate model | Adjusted modela | Univariate model | Adjusted modela | |
| No frailty and no sarcopenia risk (n = 270) | 1.000 | 1.000 | 1.000 | 1.000 |
| Frailty or sarcopenia risk (n = 238) | 3.757 (0.770−1.646) | 2.324 (1.227−4.401) | 4.001 (2.432−6.582) | 2.562 (1.444−4.547) |
| Frailty and sarcopenia risk (n = 145) | 12.329 (7.043−21.580) | 8.008 (4.097−15.653) | 17.527 (10.281−29.879) | 12.396 (6.650−23.105) |
APACHE acute physiology and chronic health evaluation, CFS clinical frailty scale.
Adjusted model included age, sex, and APACHE II score.
3.3. Incremental predictive value of adding the CFS score and SARC-CalF score to the APACHE II score for mortality outcomes
The CFS, SARC-CalF, and APACHE II scores all demonstrated good discriminative ability for ICU mortality [area under the receiver operating characteristic curve (AUROC) = 0.745, 0.785, and 0.789, respectively] (Fig. 1). The addition of the CFS, SARC-CalF, and CFS + SARC-CalF scores to the APACHE II score significantly improved its ability to identify those at risk of ICU mortality (all p for Δ C-statistic < 0.001; Table 3; Fig. 1). When the CFS, SARC-CalF, and CFS + SARC-CalF scores were included, the discriminative ability of the APACHE II score was improved with an IDI of 0.102, 0.144, and 0.153, respectively (Table 3). The net improvement in predicted probabilities was also increased significantly (NRI = 0.709, 0.757, and 0.933 for the addition of the CFS, SARC-CalF, and CFS + SARC-CalF scores, respectively; Table 4).
Fig. 1.
Area under the receiver operator curve for ICU and 30-day mortality.
Table 4.
Incremental value of sarcopenia and frailty for ICU mortality and 30-days mortality.
| Δ C-statistic (95% CI) | P for Δ C | IDI (95% CI) | NRI (95% CI) | |
|---|---|---|---|---|
| ICU mortality | ||||
| CFS score + APACHE II vs. APACHE II | 0.059 (0.029−0.090) | <0.001 | 0.102 (0.074−0.129) | 0.709 (0.534−0.884) |
| SARC-CalF score + APACHE II vs. APACHE II | 0.076 (0.042−0.111) | <0.001 | 0.144 (0.113−0.175) | 0.757 (0.606−0.908) |
| CFS score + SARC-CalF score + APACHE II vs. APACHE II | 0.081 (0.046−0.116) | <0.001 | 0.153 (0.121−0.186) | 0.933 (0.767−1.099) |
| 30-days mortality | ||||
| CFS score + APACHE II vs. APACHE II | 0.099 (0.061−0.136) | <0.001 | 0.154 (0.123−0.185) | 0.758 (0.601−0.916) |
| SARC-CalF score + APACHE II vs. APACHE II | 0.121 (0.083−0.159) | <0.001 | 0.207 (0.174−0.239) | 0.853 (0.714−0.992) |
| CFS score + SARC-CalF score + APACHE II vs. APACHE II | 0.129 (0.090−0.169) | <0.001 | 0.226 (0.191−0.261) | 0.998 (0.847−1.148) |
APACHE acute physiology and chronic health evaluation, CFS clinical frailty scale.
The CFS, SARC-CalF, and APACHE II scores also showed good discriminative ability for 30-day mortality (AUROC = 0.764, 0.814, and 0.746, respectively) (Fig. 1). The addition of the CFS, SARC-CalF, and CFS + SARC-CalF scores improved the ability of the APACHE II score to identify those at risk of 30-day mortality (Table 3; Fig. 1). When these scores were included, the discriminative ability of the APACHE II score was significantly improved, and the net improvement in predicted probabilities was also increased significantly (Table 4).
4. Discussion
This study prospectively collected clinical data from 653 critically ill patients and conducted a short-term follow-up. For the first time, we found that the SARC-CalF score and sarcopenia risk defined by this score were independent risk factors for the ICU mortality and 30-day mortality of older critically ill patients. Similarly, our study demonstrated that the CFS score and CFS-defined frailty were significantly associated with the short-term mortality of older critically ill patients. In addition, the study showed that a combination of the SARC-CalF and CFS scores significantly improved the predictive ability of the classic APACHE II score for the short-term mortality outcomes of older critically ill patients.
Previous studies have examined the prognostic stratification value of frailty assessment for critically ill patients. Muscedere et al. were the first to conduct a meta-analysis including 10 cohort studies and 3030 patients to determine the association of frailty with poor prognosis in critically ill patients [9]. The results showed that the prevalence of frailty among critically ill patients was around 30% [9], which is consistent with the result of our study (29.5%). In addition, Muscedere et al. observed a significantly higher risk of in-hospital mortality among frail patients than among non-frail patients [9]. It should be noted that the studies included in the above meta-analysis used different frailty assessment tools to define the frailty status of patients. Recently, a meta-analysis based on individual case data investigated the effect of CFS-defined frailty on the prognosis of critically ill patients [15]. This meta-analysis included 12 studies and 23,989 patients and found a significantly higher risk of ICU mortality among older critically ill patients with CFS-defined frailty than among non-frail patients [15]. Some studies have examined the incremental value of frailty in predicting the mortality outcomes of critically ill patients. Bai et al. used the MIMIC database to determine the predictive value of frailty in predicting the poor prognosis of patients with critical acute myocardial infarction [16]. The study found that the hospital frailty risk score (HFRS), which is a frailty assessment tool derived from electronic medical records, could significantly improve the predictive ability of the Sequential Organ Failure Assessment (SOFA) score for the in-hospital and 1-year mortality of patients with critical acute myocardial infarction. Another study by Bai et al. found that the frailty index based on laboratory tests (FI Lab) could significantly improve the predictive ability of various disease severity scoring systems including the SOFA score, Acute Physiology Score (APS) 3, Simplified Acute Physiology Score (SAPS) II, Logistic Organ Dysfunction Score (LODS), and Oxford Acute Severity of Illness Score (OASIS) for the in-hospital and 1-year mortality of patients with critical acute myocardial infarction [17]. However, a study conducted by Subramaniam et al. showed that CFS-defined frailty could not significantly improve the predictive ability of the Australian and New Zealand Risk of Death (ANZROD) model for the 1-year mortality risk of critically ill patients [18]. Our study showed that the CFS score significantly improved the predictive ability of the APACHE II score for the ICU mortality and 30-day mortality of critically ill patients. Some possible reasons for the contrasting research results may be as follows: (1) heterogeneity in age and disease severity among patients included in different studies; (2) different disease severity scoring systems in different studies. Overall, there is still a lack of research on the incremental value of frailty in predicting adverse outcomes for critically ill patients, which warrants further investigation in future studies.
Similar to frailty, sarcopenia is an important geriatric syndrome and an independent risk factor for poor prognosis in patients with various diseases [3]. Previous studies have observed the significant association between sarcopenia and the in-hospital, 30-day, and 1-year mortality of critically ill patients, which was diagnosed through secondary analysis of chest and abdominal CT images. A recently published meta-analysis showed that among critically ill patients receiving MV treatment, sarcopenia significantly increased the risk of mortality, MV treatment time, ICU hospitalization time, and total hospitalization time [12]. Research on sarcopenia in critically ill patients is usually based on the post-hoc analysis of CT images; thus, it is not possible to conduct sarcopenia assessment at the point of patient admission for timely risk stratification.
Some patients who have not undergone chest and abdominal CT examination are also unable to undergo sarcopenia assessment, greatly limiting risk stratification for critically ill patients. A few studies have investigated the application of bioelectrical impedance analysis (BIA)-based diagnosis of sarcopenia in critically ill surgical patients. A study by Vongchaiudomchoke included 120 surgical ICU inpatients aged over 65 years and applied BIA to assess muscle mass and diagnose sarcopenia [19]. The results showed a significant association between sarcopenia and 120-day mortality [19]. Yuenyongchaiwat et al. included 160 patients who underwent open chest surgery and performed BIA to measure the muscle mass of the included patients [20]. In comparison with patients without sarcopenia, patients with sarcopenia had a longer MV treatment time and hospital stay [20]. Currently, no study has used BIA methods to evaluate sarcopenia in critically ill medical patients, which may be mainly attributed to the use of various pipes, such as endotracheal intubation, ventilator line, indwelling venous passage, and drainage tube. The vast majority of BIA equipment requires patients to stand on the machine and maintain their position for at least 3–5 min for muscle mass measurement, which limits application for critically ill patients. In comparison with the diagnosis of sarcopenia based on CT images and BIA, SARC-CalF can assess the sarcopenia risk of critically ill patients upon admission to the ICU, which provides prognostic information through a questionnaire completed by patients or their proxies. The use of the SARC-CalF questionnaire for critically ill patients should be further investigated in future large-scale, prospective studies.
Our study has important clinical implications. Both CFS and SARC-CalF were clinical assessment that can be immediately available after patients’ admission to the ICU and does have good validity for differentiating short-term survivors from those who die, even before the calculation of acute illness severity such as the APACHE II score. Furthermore, our study clarified that patients had both frailty and sarcopenia had poorer outcomes than those had not or had only one condition. This implies that more timely and effective intervention is needed for this patient group.
The main strength of this study is that it is the first to evaluate the prognostic value of the SARC-CalF questionnaire for older critically ill patients. In addition, we determined the incremental predictive value of combining the SARC-CalF and CFS scores with the APACHE II score for mortality outcomes. Our study results have important guiding significance for clinicians to make treatment plan decisions. However, our study also has some limitations. First, the present study was a single-center study, and the study sample size was relatively small. Second, our follow-up duration was short, and future research should further investigate the predictive value of the SARC-CalF score for the long-term mortality outcomes of critically ill patients. Third, the SARC-CalF questionnaire was a screening questionnaire for sarcopenia risk. Therefore, the incidence of sarcopenia in patients may have been overestimated.
5. Conclusions
Sarcopenia risk assessed by the SARC-CalF questionnaire provided independent prognostic information in a single-center cohort of older critically ill patients. A combination of sarcopenia and frailty might enhance the ability of classic disease severity scoring systems in predicting negative outcomes for older critically ill patients. Future studies should further evaluate the application of the SARC-CalF questionnaire for critically ill patients.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of Henan Provincial People’s Hospital (2021-No-00049). Written informed consent was obtained from all included patients and/or their proxies before study participation.
Consent for publication
Not applicable.
Availability of data and materials
Reasonable requests for conditional reuse of the data can be submitted to the corresponding author.
Conflict of interest
None declared.
Author contributions
WMB, JX and LJQ designed and supervised the study. WMB and HBG curated and harmonized the data. WMB and HBG performed statistical analyses. WMB and HBG drafted the manuscript. WMB, HBG, HH and JX critically reviewed all statistical methods, procedures, and results. WMB and LJQ critically revised the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
Not available.
Acknowledgments
Not available.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2024.100321.
Contributor Information
Juan Xu, Email: sophia2932@163.com.
Lijie Qin, Email: qinlijie1819@163.com.
Appendix A. Supplementary data
The following is Supplementary data to this article:
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